import cv2 import numpy as np from config.CONFIG_UIED import Config C = Config() def read_img(path, resize_height=None, kernel_size=None): def resize_by_height(org): w_h_ratio = org.shape[1] / org.shape[0] resize_w = resize_height * w_h_ratio re = cv2.resize(org, (int(resize_w), int(resize_height))) return re try: img = cv2.imread(path) if kernel_size is not None: img = cv2.medianBlur(img, kernel_size) if img is None: print("*** Image does not exist ***") return None, None if resize_height is not None: img = resize_by_height(img) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) return img, gray except Exception as e: print(e) print("*** Img Reading Failed ***\n") return None, None def gray_to_gradient(img): if len(img.shape) == 3: img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) img_f = np.copy(img) img_f = img_f.astype("float") kernel_h = np.array([[0,0,0], [0,-1.,1.], [0,0,0]]) kernel_v = np.array([[0,0,0], [0,-1.,0], [0,1.,0]]) dst1 = abs(cv2.filter2D(img_f, -1, kernel_h)) dst2 = abs(cv2.filter2D(img_f, -1, kernel_v)) gradient = (dst1 + dst2).astype('uint8') return gradient def reverse_binary(bin, show=False): """ Reverse the input binary image """ r, bin = cv2.threshold(bin, 1, 255, cv2.THRESH_BINARY_INV) if show: cv2.imshow('binary_rev', bin) cv2.waitKey() return bin def binarization(org, grad_min, show=False, write_path=None, wait_key=0): grey = cv2.cvtColor(org, cv2.COLOR_BGR2GRAY) grad = gray_to_gradient(grey) # get RoI with high gradient rec, binary = cv2.threshold(grad, grad_min, 255, cv2.THRESH_BINARY) # enhance the RoI morph = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, (3, 3)) # remove noises if write_path is not None: cv2.imwrite(write_path, morph) if show: cv2.imshow('binary', morph) if wait_key is not None: cv2.waitKey(wait_key) return morph